Information analysis is a time-consuming process of searching, evaluating, and transforming massive amounts of raw information into descriptions, hypotheses, and explanations. Given the imperfection of today's information technologies, finding relevant information in an evolving investigation is even more challenging for two main reasons.
First, analysts cannot use today's tools to efficiently find often scattered but related information. This becomes more evident when analysts do not know how to express their precise information interests (e.g., initially they may not know what to look for), or the desired information (e.g., finding suspicious financial transactions) cannot be obtained directly from available data sources.
Consider the investigation of an alleged city election fraud. Following a tip that a high-tech company may be involved, Mia, the analyst, discovers a recent biotech startup called Boynton and an alleged land grab event involving Swiss developers. As the investigation develops, Mia also learns that city attorney Rinz is a Swiss native and often involved in making decisions on city land use. Hoping to link the two events together, Mia would like to find more information on Rinz in the context of Boynton and the land grab discoveries. However, the connections among the three entities, Rinz, Boynton, and land grab, may be indirect. Thus, simply combining all the keywords together, such as “Rinz Boynton Land Grab,” may not produce any results.
Since existing information analysis tools typically do not understand and maintain a fine-grained, user context, Mia must manually craft multiple inquiries to find the desired information. For example, she may first search the news reports on “land grab.” She then combines “Rinz” with terms found in the reports to search for Rinz. Moreover, she must repeat the process to find out more on Rinz in the context of Boynton.
Second, analysts cannot use today's tools to easily manage their evolving information desires in an investigation. Due to incomplete and inconsistent information, analysts often conduct a non-linear investigation by maintaining multiple investigative threads. In the above example, Mia maintains two threads, one on Boynton and the other on land grab. As the investigation evolves, analysts may want to find information that connects different threads. Assume that Mia discovers that Rinz, who may be involved in the land lab, also heavily invests in a venture capital group that finances Boynton. Based on this lead, Mia wishes to uncover more linkages between Boynton and the alleged land grab. However, no existing tools would automatically retrieve the desired information for Mia.
Moreover, analysts may need to re-evaluate past information in the current context as the investigation evolves (e.g., discovery of new information). For example, initially phone calls from the city hall to the city attorney and Switzerland may appear innocuous. However, after Mia discovers that the alleged land grab may involve both Swiss developers and the city attorney, the relevant phone records may become important clues. Again, no existing tools would automatically re-evaluate previously retrieved information (e.g., the phone records) in the updated context. As a result, users would not be alerted to re-examine the newly surfaced, relevant information.
Accordingly, improved information analysis techniques which overcome the above or other drawbacks are needed.